Data for humanity

Monday, May 11, 2015

18:00 - 21:15

Big Data is changing the business world. Uber is killing the traditional taxi business. Twitter has upended the newspaper industry. Big data is transforming the retail, manufacturing, and healthcare sectors.  And with an estimated half a percent of the world’s data being analysed, we may just be at the tip of the iceberg.

Big data wins because it produces better information at lower cost, which should make it a valuable tool for companies as they address social and environmental issues. And with some pioneer companies putting data to good use, we convened some of the leading experts on the 11th May to ask how data can enable a better world.

We opened with a keynote from Kenneth Cukier, Data Editor for the Economist, and whose book “Big Data: A Revolution That Will Transform How We Live, Work and Think” is the top selling book on the subject. His TED talk, “Big Data is Better Data”, has been watched by nearly a million people.

Moderated by Peter Knight, Chairman of the Context Group, our panel included;

  • Kenneth Cukier, Data Editor for the Economist
  • Sally Fuller, Head of Product Management, Vodafone Global Enterprise
  • Ann Ewasechko, Global Manager, Living Progress Corporate Affairs, Hewlett Packard
  • Emma Prest, General Manager, DataKind UK
  • Rob Frost, Policy Director (Medical Policy), GlaxoSmithKline

We discussed issues such as;

  • Should data be recognised as a key part of sustainability programmes?
  • Where are the greatest social and environmental opportunities for data?
  • How can organisations put their existing data to better use?
  • To what degree should socially useful data be seen as pre-commercial and open?
  • Will big data replace the traditional consultancy market?
  • How can we mitigate the dark side of big data?

Following the panel we broke into 10 discussion groups to share experience on how big data can be used in different areas of business strategy.


Ann Ewasechko Hewlett Packard

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Emma Prest DataKind UK

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Kenneth Cukier The Economist

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Peter Knight Context

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Rob Frost GlaxoSmithKline

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Sally Fuller Vodafone Global Enterprise

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Round Tables

The role of data in a sustainability strategy

This table will draw inspiration from the panel discussion and consider different ways that data can enhance a typical organisation’s sustainability strategy. Building data dashboards, sharing sustainability data externally, machine to machine communication, using data to influence customer demand & more. How big is the data opportunity, and are sustainability teams on top of this issue?

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• Big data sharing can generate competitive advantage– in making data available and accessible to the wider public, which can then take it, analyse it and add value to it at no cost for the company. If the data is anonymised, there is minimal risk. For example, ‘Bristol is Open’ initiative – through EU funding the Bristol City Council in cooperation with universities is making various datasets publically available, from monitoring systems in street lightings to traffic flow from mobile phones systems. In this way, the Green Capital of Europe in 2015 will get competitive advantage from the subsequent analysis of this data released in the public space
• Big data can be a source of innovation – in creating linkages between areas that didn’t initially have one using unstructured (qualitative) data. For example, start-up company eRevalue specializes in helping organisations identify what the most material and emerging issues facing them are through deriving trends out of processing unstructured data (everything from reports, filings, regulatory landscapes, social media as the gateway to internet). 5 years ago it was all about carbon, now it is about corporate slavery, net neutrality and more. Some companies have also volunteered anonymised internal data such as employee surveys on the condition that only aggregate data will be released. The company is founded on the premise that most sources out there are not fully processed and activated.
• Big data can help identify target market – for example using datasets from Google maps to identify rooftop space and radiation can be linked with the UK renewables feed-in tariffs to identify and target those people that would have the highest business case out of renewable installation. If they don’t have sufficient funds to invest, they can be linked up with the investment banking industry ultimately unlocking deals and ensuring a sustainability win for the country
• Big data can shape the business case for sustainability – Companies need a business case to get started and big data can help them. For example the Curve is a Crowd platform that allows companies to share basic data on internal projects, such as technical and return on investment experience on their low carbon energy investments (everything from lighting to renewables). As another example, environmental charity Global Action Plan, which is using behaviour change to help organisations embed and understand sustainability, has completed a project with O2 (Square 1) to build the business case of employee engagement on sustainability. In the future other companies are using it and adding to the business case so sustainability managers have more data to build a better story.

• Good quality data that is constantly maintained and updated comes at a (high) cost
• Most companies produce a lot of data but do not then comprehensively capture it and use it; when they do different departments don’t always talk to each other
• At the same time we need to be mindful of how much data is being requested and captured so as not to overburden the analysis
• There is often a need for a pain/frustration point at the organisation level to induce involvement with big data – one has to be looking for something already and big data can help provide the answer

Digital Revolution: Divider or Unifier?

Our society is threatened by the rapidly increasing gap between the haves and have-nots. How, if at all, can the digitizing of modern life help close the divide?  Does business have a role to play by ensuring its data does good for all?

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• Obstacles
One company had three main sources of data – consumers, SMEs and corporate. Consumers sign Terms and Conditions for their data, but in many cases are likely not to read or understand them. It was suggested that people need to know how the data is going to be used and that it is to be used positively. Positive examples could encourage others to make data available.

• Warnings
One participant discussed that the “Internet of Things” might only be available to those who have the means and skills to use the data and general agreement was that security and safety of data minimising risk was one of the key barriers to Big Data being a unifier or for being used for the greater good. There were comments that companies were risk averse and that organisations would be concerned about being the first mover (giving away the “crown jewels” to competitors).

• Solutions
There is also the issue of what is the definition of the common good? How is this agreed as there is a governmental and legislative aspect. Many large companies tend to be risk averse, particularly where customer data was concerned. A research consultant at The Social Innovation Partnership highlighted that there was a duty to share information, and sometimes this was a legal duty, for example in disaster management and for government projects.
An important issue was raised in terms of who wants the data if it is offered up? Also the common good will present itself over time but it is not clear at the moment what unifying with data means. The Founder of talked about the triple bottom line as where the benefits have to be seen and brands have to be seen to do the right thing.

• Opportunities
There is also the issue of what is the definition of the common good? How is this agreed as there is a governmental and legislative aspect. John said that many large companies tended to be risk averse, particularly where customer data was concerned.

• Examples
The Director of Corporate Citizenship at Samsung suggested that IT was a great leveller and there were efforts to level the playing field to solve the problem of youth unemployment in Europe. There was however the issue of stringent privacy laws particularly in Europe and there were scare stories in the media regarding use of information – eg voice recording TVs.
The Plan A team at M&S has responsible sourcing data, this could be seen as competitive advantage and not shareable so legislation would make it easier and transferable to the wider business community.

Data in the supply chain

Building a sustainable supply chain today can be daunting and time-consuming. We ask this table to consider whether big data will change this, improving business decisions from an environmental and social perspective, and at a much lower cost?  Will greater supply chain data and sophisticated analytical tools, like the Water Risk Monetizer, combine to replace expensive experts?

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Attendees raised the following as key issues for data to be relevant in the supply chain:
• What incentives are needed for more transparency in and between companies?
• Can data be used to improve traceability through the supply chain?
• Can data have impact in lower supply tiers where data is harder to generate and gather?
• When does supply chain data become ‘big data’ and when will it be of a quality that is useable for positive impacts?

• Compliance for addressing supply chain impacts alone is not working – more incentives and less force is needed. Investing in sustainable supply chains has to pay for the company.
• Huge sensitivity over sharing data - exposure is damaging. Do companies want to open themselves up to this by sharing their supplier issues openly?
• Supply chains for many commodities are hugely fragmented e.g coffee, cocoa, precious metals rely on smallholder farming. Programmes addressing these have become a point of competition (e.g. Sustainable Cocoa Initiative, Cocoa Life etc) - collating enough relevant data requires competitors to share their investment on intelligence and differentiation.
• Lack of public interest makes the value of investing heavily difficult to communicate internally and externally.

• Big opportunity and value from being able to ‘digest’ vast quantity of new data.
• Trade-off of heavy investment in data = increased efficiency.
• Could compliance be used to generate more data? Huge amounts of data passed on to supply chain certifiers, auditors and mappers – there’s space there for a data channel to collate sustainability data.
• Many companies are very good at using big data to incentivise customers (e.g. customer loyalty cards). The same approach used with suppliers is an avenue that needs exploring.
• Many companies are demanding data from their suppliers relating to KPIs and basing their continued business on this. Platforms such as Ecovadis are collating huge amounts of supplier data because of this.

• Sustainable supply chains will be driven either by data sharing or by key individuals.
• At an industry level, the approach is not macro enough to collaborate on shared data. At company level, there is lots of data but not enough being done with it.
• Information is core to companies operating at their best. People need the space and the freedom to solve these big problems, it cannot be forced or happen overnight.

How can data drive sustainable consumption?

The use of data to influence consumption patterns is an emerging science, and this table will discuss the degree to which this is an opportunity for sustainability teams.  Examples include; sensors that track in-store behaviour, loyalty schemes that reward better consumption, better display of social/environmental data on products . How careful do companies need to be about manipulating demand?

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The use of data to influence consumption patterns is an emerging science, but to what degree does it present an opportunity for sustainability teams? Examples include; sensors that track in-store behaviour, loyalty schemes that reward better consumption, better display of social/environmental data on products. How careful do companies need to be about manipulating demand?

• How truly aligned is the concept of big data with sustainable consumption? As data enables ever more sophisticated personalised marketing, will it ultimately just drive us to consume more and more?
• Data can play an important part in promoting sustainability by influencing choice. And the more we know, the more we can target by trigger points.
• However, we need to target at the heart of what is really important to the consumer in order to engage. The data needs to relate to the lifestyle benefits of sustainable consumption.
• Energy companies are starting to see changes in behaviour linked to providing comparative behaviour i.e. how much energy you are using compared to your neighbour. It’s about providing personalised data aligned to key motivators.
• If by sustainability, we mean the sustainability of mankind then could data promote healthy living? Can big data make us thinner? With one of the western world’s biggest problems obesity, could we see a time when purchasing habits are manipulated in-store towards healthier choices, or through considered vouchering? How does this fit with fears surrounding a ‘nanny state’?
• Does the ‘end’ of sustainability justify the ‘means’ of giving up your data? Privacy represents one of the biggest issues surrounding big data – from who actually owns it to how it should be ethically used.
• Do people understand the value of their data and will there become a time when it is monetised –might people be prepared to share more, to pay less for goods and services?
• Does cross-border regulation need developing? If unscrupulous organisations mistreat data do we risk losing the trust of society in the potential benefits, if we don’t act soon enough to control its use?

Tomorrow’s sustainability leader

The adage “if you can measure it, you can manage it” is being taken to new levels with sustainability dashboards. This table looks 5 years into the future, and considers how a leading sustainability expert will use data – the quality of data capture, the degree of supply chain visibility, industry benchmarking, M2M connectivity, the device they’ll be using and more. A nice-to-have or a critical solution?

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1. Obstacles
Sharing data externally as well as internally between business divisions/departments
Going into the unknown – collecting or sharing data with no known uses. Potential consequences.
The ability to transform data into something usable or easily understandable. For example, employees volunteering two days of their time and how to communicate the benefits of this through a dashboard
The need to benchmark and knowing the right metrics to use
Companies get too bogged down in the quality of data and are not able to see the bigger picture

2. Warnings
Finding the right data and presenting it in a way that is meaningful to the right group of people
Knowing the final use of the data and proving its value beforehand – linking it to a specific decision or answering a specific business challenge
Sustainability dashboards have gone beyond awareness raising now. They should be in the realm of analytics, telling you the meaning of the data and what to do with the data
Idea of the dashboard seems ageing and may become out of data in 5-years. Dashboards should be about what you do with the data
Current capability gap in knowing how to present the same data in different ways to different stakeholders in your organisation
You will always have people that don't believe the data

3. Solutions
Want to be able to compare to peers that aren't reporting any data
GIS is a potential solution but there are limited ways to use it though. Want to introduce economic factors with this information so need for a dashboard that lets you use GIS easily and integrated with other datasets.

4. Opportunities
Sharing information with your competitors to reduce reporting burden
Direct disclosure from employees. Empowerment will enable the reporting of more data and increase visibility in areas which are currently ‘data-less’
Dashboards are needed because you need to prioritise risks. Need to be able to work out the probability of something impacting your company financially
Need dashboards that just get to the point/meaning of the data – what is it telling you and what should I do about it?
Dashboards need to think about how data will change the way businesses think
The most obvious utility of GIS is when monitoring water scarcity and using real-time monitoring data.
The dashboard should do the analytics for you. You input what you want to know and the dashboard does the thinking and presents the limitations.
Use of equivalencies is important to engage stakeholders

5. Examples
Historic futures – supply chain collaboration
Labour voices app - allows workers to share information about a company. Similar to glass door.

Energy: using big data to unlock a market failure

With the extraordinary progress being made in big data analytics, the gap between the way organisations use energy data and companies like Google use data is growing. Does the table agree there is now a major opportunity for companies that “get” the potential for energy data, and what are the top 3 ingredients of tomorrow’s data savvy energy strategies?

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Discussion: The big opportunity for big data

• Air B&B is a poster child for the benefits of big data by helping to connect people to property that would otherwise be left empty and unused. But what is interesting about this? That the company is using data to link things up?

• Some participants felt that companies such as Air B&B and Uber are providing the platform that enables data to be used for good, but aren’t actually doing anything with it. Others disagreed and felt that these companies are providing a controlled and convenient service that a person would otherwise be unable to get by connecting “the bottom with the top”.

• Big data has big potential for property management. If we could better understand the issues that tenants are having in real-time, and channel this information into a portal, it could be aggregated and used to identify problem areas and target initiatives more effectively. Using big data, companies could also better understand what people would be willing to do without and develop incentives to encourage them to act on this by, for example, turning off the heating. Maximising the benefit of big data in property management will require us to:

➢ Interpret data in a relevant way. The average building management system is collecting a vast quantity of data - but this is very hard to interpret. While there is technology that can help do this, it doesn’t go far enough. It needs to be integrated into a company’s management system and be able to identify and interpret information that is relevant to a specific company.
➢ Display it visually. This is a really important way of engaging people on energy issues. For example, using a thermal imaging camera to show where properties are losing heat had a significant impact on making people reduce energy use. A picture can make the message more meaningful.
➢ Use data to identify trends in behaviour. By taking information from Facebook or Google, for example, we can understand people’s patterns of behaviour and help predict where energy use peaks are likely to occur. This could be valuable in geographies where energy supply from the grid is insecure.
➢ Make the potential saving more obvious. People are used to paying for utilities and only recently started thinking about ways to save money. The perception is still often that you can’t control the costs - so we need better benchmarking data on energy use and what could be achieved from energy efficiency measures, to have a big impact.

Data collaborations to tackle challenges

When organisations use their own data, their potential to create change is limited. But if companies, NGO’s and governments identify challenges and share data, there is an opportunity to see patterns and tackle the global drivers. What are the obstacles that need to be overcome, what issues can be tackled in this way, and how powerful a solution might this be in a sustainable future?

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The following WWF case study was used as a basis for starting off the discussion. The WWF uses Big Data in its fight against poaching and illegal wildlife trade. Ranger patrols collect and subsequently share data on animal movements. SMART approaches (SMART Connect & SMART in the Cloud) are used to pool various data sources together (including satellite imagery of wildfire locations).

Attendees identified the following CHALLENGES for data collection/sharing:
- Human resources devoted to collecting data (often small teams),
- Delays in uploading the data (due to lack of internet access),
- Remoteness of areas in which data is being collected,
- Size of areas in which data is being collected (infrastructure),
- Energy-supply (affecting connectivity),
- Impact on local communities (with reference to the case study, both ethically – through the perceived favouritism towards animal welfare over community welfare – and practical, such as lack of energy/connectivity experienced by local community),
- Land ownership issues (which can prevent providers from setting up infrastructure and/or encourage removal by local communities),
- Supply chain traceability (with reference to the case study, tracing individual poachers, poached good),
- Cost (including with reference to the case study, that of satellite imagery), some organisations may not afford to share their data for free,
- Convincing parties to collect and share data,
- Data ownership (Do consumers ever own their data? Do customers consent to sharing their data? Should data subjects be rewarded financially when providing data? E.g. loyalty cards require the provision of customer information but, in return, offer certain advantages),
- Data anonymity (How is data anonymised?),
- Data abuse (if it falls into the ‘wrong’ hands, e.g. with reference to the case study, the poachers’ hands),
- Concern over sharing data with government bodies in case it becomes public (incl. through Freedom of Information Requests),
- Some organisations share data for profit, which attaches a negative connotation to Big Data,
- Lack of widespread collaboration to create evidence of further positive data sharing experiences,
- Failure to process data efficiently or reliably.
Attendees agreed that data sharing is currently largely driven by transparency obligations (or profit).

- Get community support (in data collection areas),
- Share infrastructure for data collection/sharing with local communities,
- Use solar power,
- Report positive data sharing stories, such as data used for vaccine reminders, travel time apps, allergy prevention (e.g. when a correlation was found between soy bean shipments and use of asthma inhalers in Barcelona),
- Government to lead the way, such as on the London Data Store ( ,
- Apply peer pressure arguments: everyone else is doing it; some musicians continue to be successful despite sharing music on Spotify, why not apply the same argument to other data?


The data analysis gap

With ½% of all data being analysed, there is an opportunity for companies to use their data for social benefit. Most companies have employer and employee data that could be shared for skilled development opportunities or data on preferences and interests that could shape demand. How can business think differently and put data to good social use? A table for left field thinkers...

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1.1 Key questions
We’re swimming in data but where does it go and how do we convert it into something meaningful? How can we make use of its secondary benefit to help other organisations/ society as a whole? How do we get it into the public realm safely and securely? What needs to be in place for companies to do this well? How do we even define social?

1.2 Challenges & limitations
• Turning data into insight, and insight into decision-making
• Pool of talent in data processing not exploited to greatest advantage
• Easy to focus on the business aspect when you’re surrounded by other businesses driven by the same incentives
• Data can be a competitive differentiator in the market: as awareness of the value of data grows, potential for companies to become less likely to share this asset openly
• Security and data protection issues: shortage of examples of trial and error that might otherwise show and encourage responsible use
• Just because you have a mass of data, doesn’t mean it’s good quality
• Never entirely able to predict what you’re going to find out; a company requires flexibility to build on what it finds so it can be put to good use
• Disruptive to consulting models: companies/organisations hire analytic teams, removing the need for consultants to gather and analyse data

1.3 Opportunities
• Several of society’s greatest challenges require greater access to data; private sector companies are in a position to use the vast amount of data they gather for social good
• Companies that take a well-organised and data-driven approach are more likely to see investments in their sustainability programme pay off
• Innovative new uses of social media and crowdsourcing could make this easier
• Collaborating with other organisations could reduce company costs to maintain data
• Ability to provide enhanced services to existing customers through wider sharing and use of data
• Investors are increasingly asked about their social and environmental impact; the conversation is shifting from ‘whether’ to ‘how’
• A company’s social impact draws in talented people and has a positive effect on employee relations
• Acts as a sales tool externally
• Analytical skills can be used in a valuable way to help non-profit organisations

1.4 Insights
• Alignment between business and social: all activities have an underlying business interest (brand visibility, reputation, creating the right networks); let’s not try and separate this out from social value, but instead find meeting points between the two
• Everyone’s doing it: non-profits, government and corporates will continue to invest big time in big data, but it remains to be seen how beneficial this could turn out to be
• Value of developing your people: companies can help increase data literacy in the non-profit sector where deep analytical talent, data-savvy managers and supporting technology personnel are lacking
• Quality of data: potential need for a data auditing/verification system to ensure reliability and relevance
• Developing structure: at the moment all efforts are bottom up; do we need some kind of top-down help? What would a policy or regulatory solution look like? Would creative commons license at least offer companies credit for sharing their data? Could an assessment of social impact somehow become part of the checklist every company goes through? Could it be integrated into the tendering phase? Could open data become an indicator for CSR? Or do we rather need something more organic: companies brave enough to experiment; something that pulls together good stories to “feed a hunger for it”, building confidence and creating momentum?

Smart meters & smart cities

Much is promised for the “smart” era, when the transport, energy, health care, water and waste sectors start to connect their data. Better performance, lower costs and lower resource consumption are key benefits. How will this work for a mainstream business? Which companies should engage, what are the data security issues and how can this become a big sustainability opportunity?

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Much is promised for the “smart” era, when the transport, energy, health care, water and waste sectors start to connect their data. Better performance, lower costs and lower resource consumption are key benefits. How will this work for a mainstream business? Which companies should engage, what are the data security issues and how can this become a big sustainability opportunity?

• Knowing where to start in finding relevant data.
• Data available but not knowing how to access it (for example with Smart motorways and railways).
• Getting over the paranoia associated with data usage.
• Not wanting to share data that may undermine your commercial position.
• No clear guidelines on using and avoiding discloser of private information.
• There are too many variables, how to define data sets.

Red flags (warnings):
• How to safely share data and knowing who to share with is vital.
• Collaboration needed in sharing but clear rules are needed.
• Lots of unknowns on how to share data effectively.
• Companies show their date but do not act upon it or know the full extent of its uses.
• Sharing the data changes the results and behaviour of the customer.

• Working with existing customers and networks on data and problems your organisation already has is a good place to start.
• Putting the data in the hands of the consumer in a form that they can understand helps gather feedback on how data is useful.
• The data is more useful when presented in the right way, tailored to the consumer or location for example.
• The more data sets available, the better you can manage a system, find patterns and interpret cross cutting trends (consumption, demand, supply).

• Anglian Water: when the data was given back to the customer in an interactive way that they could see their baseline usage, water usage went down. Data is now being used to see whether in the longer term this behaviour change sticks.
• Data is not there yet, but collaboration between highway agencies, traffic cameras and GPS will make driverless cars not only possible but also safer than the roads are today.
• The ‘Keep it Clear’ Programme.
• Schools that lack funding in impoverished areas do not currently share data. As there is no commercial self-interest, these social issues go unheeded.

Data reporting – a way to build trust?

As companies generate increasing levels of sustainability data, the question moves on to who should have access that data and in what form. How can data build trust with stakeholder groups and society? Should there be a tailored approach to sharing data dependent on the stakeholder group? Is there a link between data reporting and innovation processes?

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Obstacles: Computerised data vs ‘human sifted’ data
- Methodology based around data reporting is questionable.
- Data sifted manually is time consuming and tedious.
- Interpretation of data: as no two people are the same, each individual will see data differently.
- Each company has different stakeholders: in reality there is more material focus on time importance.
- Given the nature of the company if the data is relevant in benefiting society is questionable for example Canon collects data how can that data be beneficial to society?
- How can one get feedback on the relevance of data
- Issues based around anonymous data:
For example: Company data is not personal data the data shared has to be approved to be shared and that involves a process of it being authorised by relevant officials.
By exposing data are you exposing your business? Issues around media coverage (as they are always looking for holes)
- Would you allow competitors to access data

- To make data better, data has to be sifted before it is passed on.
- As far as automated data is concerned there is no issues based around it.
- To get good data it is important that data is developed for business, ensure data is accurate for the use of business.
- Make data available to people and let people decide its relevance.
- Make clauses that state if data is shared, the company expects feedback of how the data became useful.
- Who you share data with is Key: Example Tesco revealed the amount of lettuce being thrown away but solutions were suggested for the issue. (Good example of innovation to seek solutions)

Red Flags:
- Realistically there is a cap on data it may not be approved to pass on or only limited data is allowed to be shared.
- As far investors are concerned there are different stakeholders groups for each investor so one data set is not relevant to all stake holders and interpreting data for all different stakeholders is challenging and timeous.
- There is no value of communicating certain metrics to certain investors.
- Everyone taking same level of generosity sharing data is questionable.
- Problems with automated systems: person recording data from automated systems may manipulate data

B Corp & Future-Fit

This table will discuss two emerging frameworks for measuring and standardising the collection of non-financial data – “B Corps” and “Future-Fit”. After an overview on both methodologies from their key architects, we debate what it will take for these kinds of standards to become widely adopted – what data should be captured, how it will be analysed etc. Will they be game-changers?

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Key B Corp Characteristics
• Key tool is B impact assessment over five areas – looks at what is core to strategy
• Score of 80/200 needed for certification – challenging
• 25000 businesses have used the tool, 1200 certified
• Perceived as for SMEs but users also include big data and e-commerce platforms
• Standards are dynamic – reviewed every two years with independent advisors
• B Corp share and develop approach with other organisations including Future-Fit
• Transparent scoring for business and when certified, their report is published online
• Inclusive approach, all businesses trying to climb a mountain using different paths
• Provides benchmarking against other businesses in the sector + collaboration opportunities
• Global – more businesses outside the US than within and a UK identity is in development

Key Future-Fit Characteristics
• Future-Fit focuses on how to reach sustainable future, whereas other rating systems focus on current best practice eg Dow Jones Sus Index (DJSI) rated oil company 85% sustainable
• Uses best available system science and sets minimum goals businesses need to achieve to get to a sustainable future
• Free to use and open source methodology, eg investors can integrate into portfolio or DJSI/Bloomberg could adopt it
• Makes clear interrelations and alignment with other systems e.g DJSI/Planetary Boundaries
• Allows for meaningful change rather than just a bit better so leaders can push forward
• Shows where business model is fundamentally flawed – where focus needed

The round table perceived the following challenges around B Corp and Future-Fit

• For these to work, businesses must make CSR core part of business and truly aligned with their business model, requiring culture change at leadership level
• The methodology (data and approach) must be accepted and embraced wholeheartedly
• There are a plethora of initiatives each with own frame/lens – which should businesses choose? Businesses must feel it is the gold standard (need big names behind it)
• How can the business upside be proven?
• Future-Fit is a set of absolutes – very challenging and does not acknowledge trade offs
• Should businesses take a more sector-specific approach rather than general?
• These standards can provide outward look but do they help engage within businesses?
• For some businesses eg B2C, supermarkets, signals are not that sustainability is a key issue

Benefits/progress relating to B Corp and Future-Fit approaches
• True leaders need critical information at their fingertips, B Corp and Future-Fit are trying to provide this in context by allowing future-looking rather than just KPI (past-orientated assessment). Using this can allow a long terms strategy to develop
• They are working on inclusivity and working together to improve their approaches
• Bcorp certified companies outperform peers
• B Corp and Future-Fit can demonstrate business commitments to decision makers
• Some companies opening data repositories -> add to the mix additional quality to the data
• Open source nature of Future-Fit so available for sustainability and open data professionals

Venue Detail

Bank of America Merrill Lynch: The Auditorium

The Auditorium | 2 King Edward Street | London | EC1A 1HQ

Please note that The Auditorium is located in the main Bank of America Merrill Lynch building. Please enter through the revolving doors into the main reception area just off Newgate Street.

Who's Attending